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Chen Q, Lu Q, Zhang L, Zhang C, Zhang J, Gu Y, Huang Q, Tang H. A novel endogenous retention-index for minimizing retention-time variations in metabolomic analysis with reversed-phase ultrahigh-performance liquid-chromatography and mass spectrometry. Talanta 2024; 268:125318. [PMID: 37875029 DOI: 10.1016/j.talanta.2023.125318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Revised: 10/07/2023] [Accepted: 10/14/2023] [Indexed: 10/26/2023]
Abstract
Consistent retention time (tR) of metabolites is vital for identification in metabolomic analysis with ultrahigh-performance liquid-chromatography (UPLC). To minimize inter-experimental tR variations from the reversed-phase UPLC-MS, we developed an endogenous retention-index (endoRI) using in-sample straight-chain acylcarnitines with different chain-length (LC, C0-C26) without additives. The endoRI-corrections reduced the tR variations caused by the combined changes of mobile phases, gradients, flow-rates, elution time, columns and temperature from up to 5.1 min-0.2 min for most metabolites in a model metabolome consisting of 91 metabolites and multiple biological matrices including human serum, plasma, fecal, urine, A549 cells and rabbit liver extracts. The endoRI-corrections also reduced the inter-batch and inter-platform tR variations from 1.5 min to 0.15 min for 95 % of detected features in the above biological samples. We further established a quantitative model between tR and LC for predicting tR values of acylcarnitines when absent in samples. This makes it possible to compare metabolites' tR from different tR databases and the UPLC-based metabolomic data from different batches.
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Affiliation(s)
- Qinsheng Chen
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, Metabonomics and Systems Biology Laboratory at Shanghai International Centre for Molecular Phenomics, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Qinwei Lu
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, Metabonomics and Systems Biology Laboratory at Shanghai International Centre for Molecular Phenomics, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Lianglong Zhang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, Metabonomics and Systems Biology Laboratory at Shanghai International Centre for Molecular Phenomics, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Chenhan Zhang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, Metabonomics and Systems Biology Laboratory at Shanghai International Centre for Molecular Phenomics, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Jingxian Zhang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, Metabonomics and Systems Biology Laboratory at Shanghai International Centre for Molecular Phenomics, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Yu Gu
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, Metabonomics and Systems Biology Laboratory at Shanghai International Centre for Molecular Phenomics, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Qingxia Huang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, Metabonomics and Systems Biology Laboratory at Shanghai International Centre for Molecular Phenomics, Zhongshan Hospital, Fudan University, Shanghai, 200032, China.
| | - Huiru Tang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, Metabonomics and Systems Biology Laboratory at Shanghai International Centre for Molecular Phenomics, Zhongshan Hospital, Fudan University, Shanghai, 200032, China.
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2
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Zhang J, Chen Q, Zhang L, Shi B, Yu M, Huang Q, Tang H. Simultaneously quantifying hundreds of acylcarnitines in multiple biological matrices within ten minutes using ultrahigh-performance liquid-chromatography and tandem mass spectrometry. J Pharm Anal 2024; 14:140-148. [PMID: 38352947 PMCID: PMC10859589 DOI: 10.1016/j.jpha.2023.10.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Revised: 09/28/2023] [Accepted: 10/14/2023] [Indexed: 02/16/2024] Open
Abstract
Acylcarnitines are metabolic intermediates of fatty acids and branched-chain amino acids having vital biofunctions and pathophysiological significances. Here, we developed a high-throughput method for quantifying hundreds of acylcarnitines in one run using ultrahigh performance liquid chromatography and tandem mass spectrometry (UPLC-MS/MS). This enabled simultaneous quantification of 1136 acylcarnitines (C0-C26) within 10-min with good sensitivity (limit of detection < 0.7 fmol), linearity (correlation coefficient > 0.992), accuracy (relative error < 20%), precision (coefficient of variation (CV), CV < 15%), stability (CV < 15%), and inter-technician consistency (CV < 20%, n = 6). We also established a quantitative structure-retention relationship (goodness of fit > 0.998) for predicting retention time (tR) of acylcarnitines with no standards and built a database of their multiple reaction monitoring parameters (tR, ion-pairs, and collision energy). Furthermore, we quantified 514 acylcarnitines in human plasma and urine, mouse kidney, liver, heart, lung, and muscle. This provides a rapid method for quantifying acylcarnitines in multiple biological matrices.
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Affiliation(s)
- Jingxian Zhang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, Metabonomics and Systems Biology Laboratory at Shanghai International Centre for Molecular Phenomics, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Qinsheng Chen
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, Metabonomics and Systems Biology Laboratory at Shanghai International Centre for Molecular Phenomics, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Lianglong Zhang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, Metabonomics and Systems Biology Laboratory at Shanghai International Centre for Molecular Phenomics, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Biru Shi
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, Metabonomics and Systems Biology Laboratory at Shanghai International Centre for Molecular Phenomics, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Men Yu
- Wuhan Laboratory for Shanghai Metabolome Institute (SMI) Ltd., Wuhan, 430000, China
| | - Qingxia Huang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, Metabonomics and Systems Biology Laboratory at Shanghai International Centre for Molecular Phenomics, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Huiru Tang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, Metabonomics and Systems Biology Laboratory at Shanghai International Centre for Molecular Phenomics, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
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3
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Ruggieri F, Biancolillo A, D’Archivio AA, Di Donato F, Foschi M, Maggi MA, Quattrociocchi C. Quantitative Structure–Retention Relationship Analysis of Polycyclic Aromatic Compounds in Ultra-High Performance Chromatography. Molecules 2023; 28:molecules28073218. [PMID: 37049982 PMCID: PMC10096086 DOI: 10.3390/molecules28073218] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Revised: 03/29/2023] [Accepted: 03/31/2023] [Indexed: 04/09/2023] Open
Abstract
A comparative quantitative structure–retention relationship (QSRR) study was carried out to predict the retention time of polycyclic aromatic hydrocarbons (PAHs) using molecular descriptors. The molecular descriptors were generated by the software Dragon and employed to build QSRR models. The effect of chromatographic parameters, such as flow rate, temperature, and gradient time, was also considered. An artificial neural network (ANN) and Partial Least Squares Regression (PLS-R) were used to investigate the correlation between the retention time, taken as the response, and the predictors. Six descriptors were selected by the genetic algorithm for the development of the ANN model: the molecular weight (MW); ring descriptor types nCIR and nR10; radial distribution functions RDF090u and RDF030m; and the 3D-MoRSE descriptor Mor07u. The most significant descriptors in the PLS-R model were MW, RDF110u, Mor20u, Mor26u, and Mor30u; edge adjacency indice SM09_AEA (dm); 3D matrix-based descriptor SpPosA_RG; and the GETAWAY descriptor H7u. The built models were used to predict the retention of three analytes not included in the calibration set. Taking into account the statistical parameter RMSE for the prediction set (0.433 and 0.077 for the PLS-R and ANN models, respectively), the study confirmed that QSRR models, associated with chromatographic parameters, are better described by nonlinear methods.
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Affiliation(s)
- Fabrizio Ruggieri
- Dipartimento di Scienze Fisiche e Chimiche, Università degli Studi dell’Aquila, Via Vetoio, 67100 Coppito, Italy
| | - Alessandra Biancolillo
- Dipartimento di Scienze Fisiche e Chimiche, Università degli Studi dell’Aquila, Via Vetoio, 67100 Coppito, Italy
| | - Angelo Antonio D’Archivio
- Dipartimento di Scienze Fisiche e Chimiche, Università degli Studi dell’Aquila, Via Vetoio, 67100 Coppito, Italy
| | - Francesca Di Donato
- Dipartimento di Scienze Fisiche e Chimiche, Università degli Studi dell’Aquila, Via Vetoio, 67100 Coppito, Italy
| | - Martina Foschi
- Dipartimento di Scienze Fisiche e Chimiche, Università degli Studi dell’Aquila, Via Vetoio, 67100 Coppito, Italy
| | | | - Claudia Quattrociocchi
- Dipartimento di Scienze Fisiche e Chimiche, Università degli Studi dell’Aquila, Via Vetoio, 67100 Coppito, Italy
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4
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Si-Hung L, Izumi Y, Nakao M, Takahashi M, Bamba T. Investigation of supercritical fluid chromatography retention behaviors using quantitative structure-retention relationships. Anal Chim Acta 2022; 1197:339463. [DOI: 10.1016/j.aca.2022.339463] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Revised: 01/02/2022] [Accepted: 01/06/2022] [Indexed: 12/11/2022]
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Tian Z, Liu F, Li D, Fernie AR, Chen W. Strategies for structure elucidation of small molecules based on LC–MS/MS data from complex biological samples. Comput Struct Biotechnol J 2022; 20:5085-5097. [PMID: 36187931 PMCID: PMC9489805 DOI: 10.1016/j.csbj.2022.09.004] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 09/03/2022] [Accepted: 09/03/2022] [Indexed: 11/06/2022] Open
Abstract
LC–MS/MS is a major analytical platform for metabolomics, which has become a recent hotspot in the research fields of life and environmental sciences. By contrast, structure elucidation of small molecules based on LC–MS/MS data remains a major challenge in the chemical and biological interpretation of untargeted metabolomics datasets. In recent years, several strategies for structure elucidation using LC–MS/MS data from complex biological samples have been proposed, these strategies can be simply categorized into two types, one based on structure annotation of mass spectra and for the other on retention time prediction. These strategies have helped many scientists conduct research in metabolite-related fields and are indispensable for the development of future tools. Here, we summarized the characteristics of the current tools and strategies for structure elucidation of small molecules based on LC–MS/MS data, and further discussed the directions and perspectives to improve the power of the tools or strategies for structure elucidation.
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6
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Bride E, Heinisch S, Bonnefille B, Guillemain C, Margoum C. Suspect screening of environmental contaminants by UHPLC-HRMS and transposable Quantitative Structure-Retention Relationship modelling. JOURNAL OF HAZARDOUS MATERIALS 2021; 409:124652. [PMID: 33277075 DOI: 10.1016/j.jhazmat.2020.124652] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Revised: 10/02/2020] [Accepted: 11/20/2020] [Indexed: 06/12/2023]
Abstract
A Quantitative Structure-Retention Relationship (QSRR) model is proposed and aims at increasing the confidence level associated to the identification of organic contaminants by Ultra-High Performance Liquid Chromatography hyphenated to High Resolution Mass Spectrometry (UHPLC-HRMS) in environmental samples under a suspect screening approach. The model was built from a selection of 8 easily accessible physicochemical descriptors, and was validated from a set of 274 organic compounds commonly found in environmental samples. The proposed predictive figure approach is based on the mobile phase composition at solute elution (expressed as % acetonitrile), that has the major advantage of making the model reusable by other laboratories, since the elution composition is independent of both the column geometry and the UHPLC-system. The model quality was assessed and was altered neither by the columns from different lots, nor by the complex matrices of environmental water samples. Then, the solute retention of any organic compound present in water samples is expected to be predicted within ± 14.3% acetonitrile by our model. Solute retention can therefore be used as a supplementary tool for the identification of environmental contaminants by UHPLC-HRMS, in addition to mass spectrometry data already used in the suspect screening approach.
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Affiliation(s)
- Eloi Bride
- INRAE, UR RiverLy, F-69625 Villeurbanne, France
| | - Sabine Heinisch
- Université de Lyon, Institut des Sciences Analytiques, UMR 5280, CNRS, F-69100 Villeurbanne, France
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7
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Haddad PR, Taraji M, Szücs R. Prediction of Analyte Retention Time in Liquid Chromatography. Anal Chem 2020; 93:228-256. [DOI: 10.1021/acs.analchem.0c04190] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Paul R. Haddad
- Australian Centre for Research on Separation Science, School of Natural Sciences, University of Tasmania, Private Bag 75, Hobart, Tasmania, Australia 7001
| | - Maryam Taraji
- Australian Centre for Research on Separation Science, School of Natural Sciences, University of Tasmania, Private Bag 75, Hobart, Tasmania, Australia 7001
- The Australian Wine Research Institute, P.O. Box 197, Adelaide, South Australia 5064, Australia
- Metabolomics Australia, P.O. Box 197, Adelaide, South Australia 5064, Australia
| | - Roman Szücs
- Pfizer R&D UK Limited, Ramsgate Road, Sandwich CT13 9NJ, U.K
- Department of Analytical Chemistry, Faculty of Natural Sciences, Comenius University in Bratislava, Mlynská Dolina CH2, Ilkovičova 6, SK-84215 Bratislava, Slovakia
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8
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Wang Z, Sakuno Y, Koike K, Ohara S. Evaluation of Chlorophyll- a Estimation Approaches Using Iterative Stepwise Elimination Partial Least Squares (ISE-PLS) Regression and Several Traditional Algorithms from Field Hyperspectral Measurements in the Seto Inland Sea, Japan. SENSORS (BASEL, SWITZERLAND) 2018; 18:E2656. [PMID: 30104512 PMCID: PMC6111830 DOI: 10.3390/s18082656] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/01/2018] [Revised: 07/22/2018] [Accepted: 08/10/2018] [Indexed: 11/16/2022]
Abstract
Harmful algal blooms (HABs) occur frequently in the Seto Inland Sea, bringing significant economic and environmental losses for the area, which is well known as one of the world's most productive fisheries. Our objective was to develop a quantitative model using in situ hyperspectral measurements in the Seto Inland Sea to estimate chlorophyll a (Chl-a) concentration, which is a significant parameter for detecting HABs. We obtained spectra and Chl-a data at six stations from 12 ship-based surveys between December 2015 and September 2017. In this study, we used an iterative stepwise elimination partial least squares (ISE-PLS) regression method along with several empirical and semi-analytical methods such as ocean chlorophyll, three-band model, and two-band model algorithms to retrieve Chl-a. Our results showed that ISE-PLS using both the water-leaving reflectance (RL) and the first derivative reflectance (FDR) had a better predictive ability with higher coefficient of determination (R²), lower root mean squared error (RMSE), and higher residual predictive deviation (RPD) values (R² = 0.77, RMSE = 1.47 and RPD = 2.1 for RL; R² = 0.78, RMSE = 1.45 and RPD = 2.13 for FDR). However, in this study the ocean chlorophyll (OC) algorithms had poor predictive ability and the three-band and two-band model algorithms did not perform well in areas with lower Chl-a concentrations. These results support ISE-PLS as a potential coastal water quality assessment method using hyperspectral measurements.
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Affiliation(s)
- Zuomin Wang
- Graduate School of Engineering, Hiroshima University, 1-4-1 Kagamiyama, Higashi-Hiroshima, Hiroshima 739-8527, Japan.
| | - Yuji Sakuno
- Graduate School of Engineering, Hiroshima University, 1-4-1 Kagamiyama, Higashi-Hiroshima, Hiroshima 739-8527, Japan.
| | - Kazuhiko Koike
- Graduate School of Biosphere Science, Hiroshima University, 1-4-4 Kagamiyama, Higashi-Hiroshima, Hiroshima 739-8528, Japan.
| | - Shizuka Ohara
- Graduate School of Biosphere Science, Hiroshima University, 1-4-4 Kagamiyama, Higashi-Hiroshima, Hiroshima 739-8528, Japan.
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9
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Retrieval of Chlorophyll-a and Total Suspended Solids Using Iterative Stepwise Elimination Partial Least Squares (ISE-PLS) Regression Based on Field Hyperspectral Measurements in Irrigation Ponds in Higashihiroshima, Japan. REMOTE SENSING 2017. [DOI: 10.3390/rs9030264] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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10
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Falchi F, Bertozzi SM, Ottonello G, Ruda GF, Colombano G, Fiorelli C, Martucci C, Bertorelli R, Scarpelli R, Cavalli A, Bandiera T, Armirotti A. Kernel-Based, Partial Least Squares Quantitative Structure-Retention Relationship Model for UPLC Retention Time Prediction: A Useful Tool for Metabolite Identification. Anal Chem 2016; 88:9510-9517. [DOI: 10.1021/acs.analchem.6b02075] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Affiliation(s)
- Federico Falchi
- Drug
Discovery and Development Department, Fondazione Istituto Italiano di Tecnologia, Via Morego 30, 16163 Genova, Italy
| | - Sine Mandrup Bertozzi
- Drug
Discovery and Development Department, Fondazione Istituto Italiano di Tecnologia, Via Morego 30, 16163 Genova, Italy
| | - Giuliana Ottonello
- Drug
Discovery and Development Department, Fondazione Istituto Italiano di Tecnologia, Via Morego 30, 16163 Genova, Italy
| | - Gian Filippo Ruda
- Drug
Discovery and Development Department, Fondazione Istituto Italiano di Tecnologia, Via Morego 30, 16163 Genova, Italy
| | - Giampiero Colombano
- Drug
Discovery and Development Department, Fondazione Istituto Italiano di Tecnologia, Via Morego 30, 16163 Genova, Italy
| | - Claudio Fiorelli
- Drug
Discovery and Development Department, Fondazione Istituto Italiano di Tecnologia, Via Morego 30, 16163 Genova, Italy
| | - Cataldo Martucci
- Drug
Discovery and Development Department, Fondazione Istituto Italiano di Tecnologia, Via Morego 30, 16163 Genova, Italy
| | - Rosalia Bertorelli
- Drug
Discovery and Development Department, Fondazione Istituto Italiano di Tecnologia, Via Morego 30, 16163 Genova, Italy
| | - Rita Scarpelli
- Drug
Discovery and Development Department, Fondazione Istituto Italiano di Tecnologia, Via Morego 30, 16163 Genova, Italy
| | - Andrea Cavalli
- Drug
Discovery and Development Department, Fondazione Istituto Italiano di Tecnologia, Via Morego 30, 16163 Genova, Italy
- Department
of Pharmacy and Biotechnology, University of Bologna, Via Belmeloro
6, 40126 Bologna, Italy
| | - Tiziano Bandiera
- Drug
Discovery and Development Department, Fondazione Istituto Italiano di Tecnologia, Via Morego 30, 16163 Genova, Italy
| | - Andrea Armirotti
- Drug
Discovery and Development Department, Fondazione Istituto Italiano di Tecnologia, Via Morego 30, 16163 Genova, Italy
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Šegan S, Opsenica I, Zlatović M, Milojković-Opsenica D, Šolaja B. Quantitative structure retention/activity relationships of biologically relevant 4-amino-7-chloroquinoline based compounds. J Chromatogr B Analyt Technol Biomed Life Sci 2016; 1012-1013:144-52. [DOI: 10.1016/j.jchromb.2016.01.033] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2015] [Revised: 01/15/2016] [Accepted: 01/19/2016] [Indexed: 12/31/2022]
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12
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Hou S, Wang J, Li Z, Wang Y, Wang Y, Yang S, Xu J, Zhu W. Five-descriptor model to predict the chromatographic sequence of natural compounds. J Sep Sci 2016; 39:864-72. [PMID: 26718117 DOI: 10.1002/jssc.201501016] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2015] [Revised: 11/18/2015] [Accepted: 12/17/2015] [Indexed: 02/02/2023]
Abstract
Despite the recent introduction of mass detection techniques, ultraviolet detection is still widely applied in the field of the chromatographic analysis of natural medicines. Here, a neural network cascade model consisting of nine small artificial neural network units was innovatively developed to predict the chromatographic sequence of natural compounds by integrating five molecular descriptors as the input. A total of 117 compounds of known structure were collected for model building. The order of appearance of each compound was determined in gradient chromatography. Strong linear correlation was found between the predicted and actual chromatographic position orders (Spearman's rho = 0.883, p < 0.0001). Application of the model to the external validation set of nine natural compounds was shown to dramatically increase the prediction accuracy of the real chromatographic order of multiple compounds. A case study shows that chromatographic sequence prediction based on a neural network cascade facilitated compound identification in the chromatographic fingerprint of Radix Salvia miltiorrhiza. For natural medicines of known compound composition, our method provides a feasible means for identifying the constituents of interest when only ultraviolet detection is available.
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Affiliation(s)
- Shuying Hou
- Department of Pharmacy Intravenous Admixture Service, the First Affiliated Hospital of Harbin Medical University, Harbin, P. R., China
| | - Jinhua Wang
- Department of Pharmacy Intravenous Admixture Service, the First Affiliated Hospital of Harbin Medical University, Harbin, P. R., China
| | - Zhangming Li
- Department of Pharmacy Administration, Harbin Medical University, Harbin, P. R., China
| | - Yang Wang
- Department of Pharmacy Intravenous Admixture Service, the First Affiliated Hospital of Harbin Medical University, Harbin, P. R., China
| | - Ying Wang
- Department of Pharmacy Intravenous Admixture Service, the First Affiliated Hospital of Harbin Medical University, Harbin, P. R., China
| | - Songling Yang
- Department of Biology Pharmacy, Heilongjiang Vocational College of Biology Science and Technology, Harbin, P. R., China
| | - Jia Xu
- Department of Nephrology, the Fourth Affiliated Hospital, Harbin Medical University, Harbin, P. R., China
| | - Wenliang Zhu
- Institute of Clinical Pharmacology, the Second Affiliated Hospital of Harbin Medical University, Harbin, P. R., China
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13
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Talebi M, Schuster G, Shellie RA, Szucs R, Haddad PR. Performance comparison of partial least squares-related variable selection methods for quantitative structure retention relationships modelling of retention times in reversed-phase liquid chromatography. J Chromatogr A 2015; 1424:69-76. [DOI: 10.1016/j.chroma.2015.10.099] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2015] [Revised: 10/27/2015] [Accepted: 10/29/2015] [Indexed: 11/27/2022]
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